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Life cycle reliability assessment of new products—A Bayesian model updating approach

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  • Peng, Weiwen
  • Huang, Hong-Zhong
  • Li, Yanfeng
  • Zuo, Ming J.
  • Xie, Min

Abstract

The rapidly increasing pace and continuously evolving reliability requirements of new products have made life cycle reliability assessment of new products an imperative yet difficult work. While much work has been done to separately estimate reliability of new products in specific stages, a gap exists in carrying out life cycle reliability assessment throughout all life cycle stages. We present a Bayesian model updating approach (BMUA) for life cycle reliability assessment of new products. Novel features of this approach are the development of Bayesian information toolkits by separately including “reliability improvement factor†and “information fusion factor†, which allow the integration of subjective information in a specific life cycle stage and the transition of integrated information between adjacent life cycle stages. They lead to the unique characteristics of the BMUA in which information generated throughout life cycle stages are integrated coherently. To illustrate the approach, an application to the life cycle reliability assessment of a newly developed Gantry Machining Center is shown.

Suggested Citation

  • Peng, Weiwen & Huang, Hong-Zhong & Li, Yanfeng & Zuo, Ming J. & Xie, Min, 2013. "Life cycle reliability assessment of new products—A Bayesian model updating approach," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 109-119.
    • Handle: RePEc:eee:reensy:v:112:y:2013:i:c:p:109-119
    DOI: 10.1016/j.ress.2012.12.002
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    1. D Briand & A V Huzurbazar, 2008. "Bayesian reliability applications of a combined lifecycle failure distribution," Journal of Risk and Reliability, , vol. 222(4), pages 713-720, December.
    2. Zhong, X. & Ichchou, M. & Saidi, A., 2010. "Reliability assessment of complex mechatronic systems using a modified nonparametric belief propagation algorithm," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1174-1185.
    3. Quigley, John & Walls, Lesley, 2011. "Mixing Bayes and empirical Bayes inference to anticipate the realization of engineering concerns about variant system designs," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 933-941.
    4. Roopesh Ranjan & Tilmann Gneiting, 2010. "Combining probability forecasts," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 71-91, January.
    5. Graves, T.L. & Hamada, M.S. & Klamann, R.M. & Koehler, A.C. & Martz, H.F., 2008. "Using simultaneous higher-level and partial lower-level data in reliability assessments," Reliability Engineering and System Safety, Elsevier, vol. 93(8), pages 1273-1279.
    6. Thomas S. Wallsten & David V. Budescu, 1983. "State of the Art---Encoding Subjective Probabilities: A Psychological and Psychometric Review," Management Science, INFORMS, vol. 29(2), pages 151-173, February.
    7. Wilson, Alyson G. & McNamara, Laura A. & Wilson, Gregory D., 2007. "Information integration for complex systems," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 121-130.
    8. Morris A. Cohen & Jehoshua Eliasberg & Teck-Hua Ho, 1996. "New Product Development: The Performance and Time-to-Market Tradeoff," Management Science, INFORMS, vol. 42(2), pages 173-186, February.
    9. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    10. Murthy, D.N.P. & Rausand, M. & Virtanen, S., 2009. "Investment in new product reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1593-1600.
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    Cited by:

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    4. Zhong, Jilong & Sanhedrai, Hillel & Zhang, FengMing & Yang, Yi & Guo, Shu & Yang, Shunkun & Li, Daqing, 2020. "Network endurance against cascading overload failure," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Tatbita Titin Suhariyanto & Dzuraidah Abd Wahab & Mohd Nizam Ab Rahman, 2018. "Product Design Evaluation Using Life Cycle Assessment and Design for Assembly: A Case Study of a Water Leakage Alarm," Sustainability, MDPI, vol. 10(8), pages 1-26, August.
    6. Wei Wang & Yaofeng Xu & Liguo Hou, 2019. "Optimal allocation of test times for reliability growth testing with interval-valued model parameters," Journal of Risk and Reliability, , vol. 233(5), pages 791-802, October.
    7. Du, Weiqi & Luo, Yuanxin & Wang, Yongqin, 2019. "Time-variant reliability analysis using the parallel subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 250-257.

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